Top-down induction of first-order logical decision trees
From MaRDI portal
Publication:1274285
DOI10.1016/S0004-3702(98)00034-4zbMath0909.68034DBLPjournals/ai/BlockeelR98OpenAlexW2033072307WikidataQ58012373 ScholiaQ58012373MaRDI QIDQ1274285
Hendrik Blockeel, Luc De Raedt
Publication date: 12 January 1999
Published in: Artificial Intelligence (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0004-3702(98)00034-4
Related Items (49)
On the use of stochastic local search techniques to revise first-order logic theories from examples ⋮ An empirical study of on-line models for relational data streams ⋮ FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data ⋮ Bagging strategies for learning planning policies ⋮ Inductive logic programming at 30 ⋮ On Tackling Explanation Redundancy in Decision Trees ⋮ Ensembles for multi-target regression with random output selections ⋮ Fast Frequent Querying with Lazy Control Flow Compilation ⋮ The complexity and generality of learning answer set programs ⋮ Algebraic Reinforcement Learning ⋮ Learning logic programs by explaining their failures ⋮ Approximate classification with web ontologies through evidential terminological trees and forests ⋮ Learning Distributional Programs for Relational Autocompletion ⋮ Learning weak constraints in answer set programming ⋮ Online learning of event definitions ⋮ Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases ⋮ Using trees to mine multirelational databases ⋮ Relational information gain ⋮ Semi-supervised online structure learning for composite event recognition ⋮ Online probabilistic theory revision from examples with ProPPR ⋮ Learning programs with magic values ⋮ Non-parametric learning of embeddings for relational data using Gaifman locality theorem ⋮ First order random forests: Learning relational classifiers with complex aggregates ⋮ Learning higher-order logic programs ⋮ Transfer learning by mapping and revising boosted relational dependency networks ⋮ Gradient-based boosting for statistical relational learning: the relational dependency network case ⋮ Data and task parallelism in ILP using mapreduce ⋮ A comparison of pruning criteria for probability trees ⋮ Fast learning of relational kernels ⋮ Incremental predictive clustering trees for online semi-supervised multi-target regression ⋮ Multi-label feature ranking with ensemble methods ⋮ kLog: a language for logical and relational learning with kernels ⋮ Top-Down Induction of Relational Model Trees in Multi-instance Learning ⋮ Feature Discovery with Type Extension Trees ⋮ Learning-driven goal generation ⋮ Type extension trees for feature construction and learning in relational domains ⋮ Knowledge discovery and data mining in toxicology ⋮ Distribution-based aggregation for relational learning with identifier attributes ⋮ Relational Sequence Learning ⋮ Seeing the Forest Through the Trees ⋮ Building Relational World Models for Reinforcement Learning ⋮ On the connection between the phase transition of the covering test and the learning success rate in ILP ⋮ Generalized ordering-search for learning directed probabilistic logical models ⋮ Inductive logic programming for gene regulation prediction ⋮ Probabilistic inductive constraint logic ⋮ Learning programs by learning from failures ⋮ Learning relational dependency networks in hybrid domains ⋮ Non-parametric learning of lifted restricted Boltzmann machines ⋮ Feature ranking for multi-target regression
Uses Software
Cites Work
This page was built for publication: Top-down induction of first-order logical decision trees
